WO2005096386A1 - Parameter adjuster - Google Patents
Parameter adjuster Download PDFInfo
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- WO2005096386A1 WO2005096386A1 PCT/JP2005/005846 JP2005005846W WO2005096386A1 WO 2005096386 A1 WO2005096386 A1 WO 2005096386A1 JP 2005005846 W JP2005005846 W JP 2005005846W WO 2005096386 A1 WO2005096386 A1 WO 2005096386A1
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- WIPO (PCT)
- Prior art keywords
- parameters
- parameter
- adjusting
- semiconductor device
- measurement data
- Prior art date
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- 238000000034 method Methods 0.000 claims abstract description 51
- 239000004065 semiconductor Substances 0.000 claims abstract description 39
- 238000005259 measurement Methods 0.000 claims abstract description 35
- 210000000349 chromosome Anatomy 0.000 claims abstract description 30
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 24
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 230000002068 genetic effect Effects 0.000 claims abstract description 21
- 238000013461 design Methods 0.000 claims description 30
- 238000011156 evaluation Methods 0.000 claims description 27
- 230000005484 gravity Effects 0.000 claims description 7
- 230000014509 gene expression Effects 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 description 15
- 238000004519 manufacturing process Methods 0.000 description 11
- 230000000694 effects Effects 0.000 description 10
- 238000004088 simulation Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000035772 mutation Effects 0.000 description 4
- 230000002759 chromosomal effect Effects 0.000 description 2
- 235000013599 spices Nutrition 0.000 description 2
- 241000652704 Balta Species 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
- H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
- H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
- H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
- H01L21/04—Manufacture or treatment of semiconductor devices or of parts thereof the devices having potential barriers, e.g. a PN junction, depletion layer or carrier concentration layer
- H01L21/18—Manufacture or treatment of semiconductor devices or of parts thereof the devices having potential barriers, e.g. a PN junction, depletion layer or carrier concentration layer the devices having semiconductor bodies comprising elements of Group IV of the Periodic Table or AIIIBV compounds with or without impurities, e.g. doping materials
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Definitions
- the present invention relates to a parameter adjustment device and a parameter adjustment method, and particularly to a parameter adjustment device capable of adjusting a large number of parameters used in a circuit design model of a semiconductor device such as a transistor in a short time. It relates to the device and the parameter adjustment method.
- the BSIM includes a plurality of parameters including variables such as the gate channel length L and channel width W and multiple parameters, including the relationship between Vg (gate voltage), Vd (drain voltage), Vb (balta voltage), and Id (drain current). It is represented by a mathematical formula.
- BSIM consists of a large number of mathematical expressions, and it is difficult to adjust parameters because there are more than 50 basic parameters to be adjusted. In addition, there is a problem that an error increases when a simulation is performed by extrapolating a region having no measured value.
- HiSIM a surface potential model developed by Miura, who is one of the inventors of the present invention, based on the surface potential based on the analytic expression based on the surface potential
- HiSIM A new type of circuit design model called Hiroshima-university STARC IGFET Model
- HiSIM which describes transistor characteristics using this surface potential
- BSIM which is expressed as a function of external voltage
- Has features The details of HiSIM and the conventional parameter adjustment method in HiSIM are described in the following URL downloadable document below, and the detailed description is omitted.
- Non-patent document 1 HiSIM 1.1.1 User's Manual
- Patent Document 1 JP 2003-108972 A
- the parameter adjusting device of the present invention has a surface potential such as HiSIM as a circuit design model of a semiconductor device. Based on the surface potential model from which the analytical formula was derived, a chromosome with each of a plurality of parameters of the circuit design model of the semiconductor device as a gene was defined, and the chromosome was used as the characteristic measurement data of the prototype semiconductor device. And a parameter adjusting means for optimizing the parameters using a genetic algorithm.
- the parameter adjusting means includes: first partial parameter adjusting means for adjusting a parameter for determining a structure of the semiconductor element based on specific measurement data of the semiconductor element belonging to the long channel group; With reference to the adjustment results of the parameter adjusting means, based on the specific measurement data of the channels of various lengths, at least the parameters that need to be adjusted other than the parameters adjusted by the first partial parameter adjusting means are adjusted. It is also characterized by the provision of the second partial parameter adjusting means.
- the parameter adjustment device of the present invention has a genetic algorithm for adjusting parameters of a circuit design model of a semiconductor device based on a surface potential that determines all transistor characteristics, such as HiSIM, due to the features described above.
- the effect is that the optimal parameter adjustment, which has been difficult in the past, can be performed in a short time and with high accuracy.
- by dividing the parameter group into two or more groups and adjusting the parameters stepwise using specific measurement data suitable for each group there is an effect that processing efficiency and accuracy are further improved. .
- FIG. 1 is a flowchart showing an overall procedure when performing a simulation using the parameter adjustment device of the present invention.
- FIG. 2 is a schematic flowchart showing the contents of a parameter adjustment process of the present invention.
- FIG. 3 is a schematic flowchart showing parameter adjustment processing using a GA.
- FIG. 4 is a flowchart showing the contents of crossover processing in S32.
- FIG. 5 is an explanatory diagram showing a crossover method of the present invention.
- FIG. 6 is a flowchart showing an example of an evaluation value calculation in S33.
- FIG. 7 is an explanatory diagram showing a method of selecting a shape of a transistor to be prototyped.
- FIG. 8 is a graph of a linear scale and a log scale showing the IdVg characteristics of a transistor.
- Fig. 9 is a list showing the technological parameters of MOSFET in HiSIM.
- FIG. 10 is a list showing mobility parameters of MOSFET in HiSIM. Explanation of symbols
- the parameter adjustment device of the present invention is realized by creating a program for executing the process shown by the flowchart described later and installing the program in any known computer system that can execute the program. Since the hardware of the computer system is well known, detailed description is omitted. Example 1 will be described below.
- FIG. 1 is a flowchart showing an overall procedure for performing a simulation using the parameter adjustment device of the present invention.
- transistors eg, different gate (channel length L and channel width W) having different shapes (sizes) in the LSI manufacturing line are used. Prototype of MOSFET).
- FIG. 7 is an explanatory diagram showing a method for selecting a shape of a transistor to be prototyped.
- the shape is selected by, for example, dividing the maximum value and the minimum value of L (channel length) and W (channel width) at equal intervals, or dividing the value closer to the minimum value more finely.
- select multiple locations (shapes) of the transistor to be prototyped on the LW plane In SI1 the electrical characteristics of the prototyped transistor are measured. Specifically, for the IdVd characteristic (fixed Vb), the IdVd characteristic (fixed Vg), and the IdVg characteristic (fixed Vd), the measurement for obtaining a plurality of sample values (measurement data) is performed a plurality of times by changing the fixed value.
- the parameter adjustment of the circuit design model of the semiconductor is performed by the method described later using the parameter adjustment device of the present invention so that the characteristics of the transistor manufactured in the manufacturing line are matched with high accuracy. Perform processing.
- the parameter adjustment device of the present invention employs, in S12, a surface potential model derived from an analytical expression based on a surface potential such as HiSIM as a circuit design model of a semiconductor device, A chromosome with each of a plurality of parameters of the design model as a gene is defined, and the parameters are optimized using a genetic algorithm based on characteristic measurement data of the prototyped semiconductor device.
- HiSIM a basic device characteristic description is obtained by solving a semiconductor basic equation.
- the parameters of the portion that complies with the device element design are parameters that determine the structure of the semiconductor element, and their physical meaning is clear, their contribution is clear in the analytical formula, and they are independent of the channel length. This is a parameter that has a significant effect on characteristics.
- the parameters introduced to reproduce the measured transistor characteristics by compensating for the imperfectness of the model and the incompleteness of the transistor do not significantly affect the characteristics for long channels! [0021] Therefore, the adjustment of the parameters of the portion according to the device element design and the adjustment of the model parameters introduced to reproduce the actually measured transistor characteristics by compensating for the imperfectness of the model and the transistor are separated. , Improve the accuracy of each parameter
- the parameter adjustment means first adjusts a parameter group for determining the structure of the semiconductor element, which greatly affects the characteristics at each channel length, based on the specific measurement data of the long channel group. Means (first step) and the adjustment result of the first parameter adjustment means, based on the specific measurement data of channels of various lengths, mainly when the channel length is short and only when the channel length is short Second parameter adjusting means (second step) for adjusting other parameters that increase the parameter. If necessary, some of the parameters adjusted in the previous stage may be adjusted in addition to the parameter groups adjusted in the next stage.
- the mobility (Mobility) parameter group includes VDS0,
- the present inventors have found that the parameters that need to be adjusted have a large effect on the characteristics at all channel lengths, the parameters that determine the structure of the semiconductor element, and the models and actual results mainly when the channel length is short.
- This parameter is used to correct the deviation of the characteristics of the Focusing on the fact that when the channel length is short, the effect is large, but when the channel length is long, the effect on accuracy is divided into small parameters, and as a first step, the characteristics are greatly affected at each channel length.
- Invented was a method of adjusting the parameters that give the parameters, and as the second step, adjusting the remaining parameters using the results of the first step.
- the five parameters of the mobility parameter group described above are not parameters that determine the structure of the semiconductor element, but are parameters that are determined by determining technical parameters, and these parameters are also determined. Adjust in the first step.
- FIG. 2 is a schematic flowchart showing the contents of the parameter adjustment (fitting) process in the present invention.
- S20 of the first step several types of measurement data of the group having the longer channel length L are read.
- the reason for using the measurement data of the group with the channel length L is that the parameters to be adjusted in the second step are not yet adjusted, so the parameters to be adjusted in the second step This is to improve the accuracy of the parameters adjusted in the first step by evaluating the accuracy of the parameters adjusted in the first step using long channel data.
- the parameters not adjusted in the first step that is, the parameters not adjusted and the parameters adjusted in the second step, are known values such as fixed values, measured values, and the like. Read the recommended values.
- a GA Genetic Algorithm
- S23 which is the second step
- measurement data of each channel length is read.
- S24 for parameters not adjusted in the second step, fixed values, measured values, etc. Read knowledge values, recommended values and parameter values determined in the first step.
- GA adjustment processing is performed on the remaining parameters to determine values.
- the parameters adjusted in the first step may be adjusted again in the second step.
- the adjustment range of the parameter may be limited to the vicinity of the value adjusted in the first step.
- the measurement data of each channel length is used for the following reason.
- adjustment of parameters having a large effect is mainly performed only on the short channel.
- the accuracy in the long channel region is reduced. Therefore, in the second step, highly accurate parameters can be obtained in all regions by using the measurement data of the long channel.
- the processing of S25 is the same as that of S22 in the power GA algorithm itself, which differs in the type and number of parameters, the number of chromosomes to be generated, and the like.
- FIG. 3 is a schematic flowchart showing a parameter adjustment (fitting) process using the GA of S22 and S25.
- S30 a measurement data group to be used is read, and N chromosomes having genes as parameters to be adjusted of the transistor circuit design model function are generated as an individual population. The generation of an individual is to determine the value of a gene in a chromosome and calculate an evaluation value of the chromosome.
- parameters such as the number of chromosomes N and the number of offspring c in the genetic algorithm are changed depending on the number n of parameters to be adjusted. This results in faster processing if n is small.
- HiSIM the range of recommended parameter initial values for each parameter is defined. For each parameter, an initial value is randomly determined within the range of the recommended parameter initial value to obtain a gene value.
- the gene value may be expressed as the logarithm of the parameter value, and the gene value may be determined logarithmically.
- An exponential search range is a search range with a large difference in the number of digits at the lower and upper limits, such as [10E-25 10E-9].
- the initial value may be limited to the range. For example, by measuring the transistor If the threshold voltage (Vth) is known, the existence range of various parameters can be estimated based on this value. By limiting the range of the initial values of the parameters of the genetic algorithm to this existence range, the search time of the genetic algorithm (the time required for convergence) can be significantly reduced.
- S31 P parental chromosomes are randomly selected from the population of individuals generated in S30.
- S32 the center of gravity G of the p parent individuals selected in S31 is obtained. That is, an average value is obtained for each parameter.
- a Child individual is generated from the selected parent individual by a crossover process described later.
- the evaluation value of the child individual generated in S32 is calculated by a method described later.
- the evaluation value of the parent individual has already been calculated.
- p are returned to the individual population from the parent individual selected in S31 and the child individuals generated in S32 in descending order of evaluation, and the rest are discarded. By this process, chromosomes with low evaluation values are selected.
- a method is used in which a part of the parent individual is returned to the population as it is without being selected, and the remaining parent individuals and child individuals are returned by the number of ⁇ remaining parent individuals '' in order of good evaluation.
- S35 it is determined whether or not the algorithm switching condition has been satisfied. If the condition has not been satisfied, the process returns to S36 if the S31 return force condition has been satisfied. Conditions include whether the number of calculations (number of generations) has exceeded a predetermined value, or whether the rate of decrease in the evaluation value (the smaller the evaluation value, the better the evaluation), is below the predetermined value. Can be raised.
- the parameters are adjusted by a known Powell method or another known local search method as a local search method. By switching the search method from GA to the local search method at the end of the search in this way, the parameter adjustment time is further reduced.
- FIG. 4 is a flowchart showing the contents of the crossover process in S32.
- This crossover method is a real-valued crossover method that generates offspring genes from polyhedrons calculated from genes of multiple parent individuals.
- FIG. 5 is an explanatory diagram showing the crossover method of the present invention.
- S40 the center of gravity of p chromosomes is calculated.
- S41 the value of the variable c is set to 1, and in S42, one offspring individual is generated from the following equation 1 using the center of gravity G and the uniformly distributed random numbers.
- p is the number of selected parent individuals
- C is a vector indicating the chromosome of the generated child individual
- Pk is a vector indicating the chromosome of the selected parent individual.
- U (0, 1) is a uniformly distributed random number in the interval [0, 1].
- FIG. 5 shows the search range of the simplex crossover when the parameters to be adjusted are a and ⁇ , and the number of parent individuals randomly selected from the individual population is three (child individual generation range).
- the center of gravity G force is also determined by multiplying the vector (L) from each parent individual ⁇ 0 to ⁇ 2 by ⁇ to determine the generation range of the child individual (the inside of the outer triangle in Fig. 4), and using the uniform random number from that range Generate The recommended value of ⁇ is ( ⁇ + 1) when the number of parent individuals is ⁇ .
- the generation range of the offspring is the internal space of the hyperpolyhedron surrounded by multiple hyperplanes.
- the parameter can be explicitly handled for a problem in which the parameter to be adjusted is a real value, and effective adjustment can be performed. Being able to handle explicitly means that an individual near the parameter space is also near the gene space.
- such crossover methods are robust to dependencies between variables and do not depend on how to take scales.Surfaces such as HiSIM, in which there are many parameters with strong dependencies between parameters and different scalings It is suitable for adjusting the parameters of model functions for circuit design of semiconductor devices, focusing on potential.
- a process called mutation is performed in addition to crossover.
- Sudden mutation is a conventional genetic algorithm that operates on discrete binary values, and performs an operation that inverts the bit values of some of the chromosomal genes.
- an operation has been proposed in which normal random numbers generated according to a normal distribution ⁇ (0, ⁇ 2) are added to each gene on a chromosome.
- the crossover method of the present invention uses a random number in the crossover process, it also has the property of mutation. Therefore, when using the above-mentioned crossover method, no mutation processing is performed.
- the chromosome evaluation value is calculated based on the error between the measured data of the prototype device and the specific data calculated by HiSIM using the gene in the chromosome as a model parameter.
- the evaluation value is determined by considering both the evaluation value on the log (logarithmic) scale and the evaluation value on the linear scale.
- FIG. 8 is a graph showing a linear scale (a) and a log (logarithmic) scale (b) showing the IdVg characteristics of the transistor.
- a linear scale
- b log (logarithmic) scale
- this part has a smaller absolute value than other parts, it is difficult to optimize this part, which also has a small absolute value of the error, using only normal linear scale data.
- the sub-threshold characteristic will be a force that can be optimized. Becomes large, and a shift occurs.
- a log-scale data group and a linear-scale data group are read simultaneously by the following scaling processing, and all characteristics are adjusted simultaneously.
- FIG. 6 is a flowchart showing an example of evaluation value calculation in which the above-described scaling and measurement data normalization techniques are implemented in S33.
- S50 the gene information of the chromosome is read and used as HiSIM model parameters.
- S51 the measurement data group is read.
- S52 the estimated data of the characteristic corresponding to the measured data is calculated based on the model parameters input in S50.
- log scale data is generated by converting the measured data group and the corresponding estimated data group into a log scale.
- the following data conversion is performed. That is, first, the maximum value ftnax and the minimum value ftnin in the data group are obtained. Next, all the measurement data 0 in the data group are converted into the normalization data g (0) according to the following Expression 2.
- g (i) normalized data
- 0 measured data
- ftnax the maximum value in the data group
- ftnin the minimum value in the data group.
- the evaluation value A of only the linear scale data group is calculated.
- A is the sum of the square errors of the normalized measurement data and the normalization estimation data.
- Log scale in S56 Calculate the evaluation value B of only the rule data group.
- B is the sum of the square errors of the normalized log measurement data and the normalized log estimation data.
- a + B is used as the chromosome evaluation value.
- HiSIM is an example of a transistor circuit design model.
- HiSIM, SP2000, OS Modell, PSP, etc. are examples of semiconductor circuit design models based on force surface potential.
- HiSIM calculates the electric charge based on the surface potential, and calculates the device characteristics using this electric charge.
- An example of such a model is the EKV model of the Swiss Technical University. Even in the case of this model, a parameter group corresponding to the technical parameter group of HiSIM can be defined, and the present invention can be applied similarly to HiSIM.
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Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
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US10/594,842 US7533356B2 (en) | 2004-03-31 | 2005-03-29 | Parameter adjusting device and parameter adjusting means |
JP2006511664A JPWO2005096386A1 (en) | 2004-03-31 | 2005-03-29 | Parameter adjusting apparatus and parameter adjusting method |
EP05727788A EP1737041A4 (en) | 2004-03-31 | 2005-03-29 | Parameter adjuster |
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JP2004-105631 | 2004-03-31 | ||
JP2004105631 | 2004-03-31 |
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WO2005096386A1 true WO2005096386A1 (en) | 2005-10-13 |
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PCT/JP2005/005846 WO2005096386A1 (en) | 2004-03-31 | 2005-03-29 | Parameter adjuster |
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US (1) | US7533356B2 (en) |
EP (1) | EP1737041A4 (en) |
JP (1) | JPWO2005096386A1 (en) |
KR (1) | KR20070003911A (en) |
TW (1) | TW200537322A (en) |
WO (1) | WO2005096386A1 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007310873A (en) * | 2006-04-18 | 2007-11-29 | Semiconductor Energy Lab Co Ltd | Parameter extraction method and computer-readable storage medium having program for executing parameter extraction method |
US8676547B2 (en) | 2006-04-18 | 2014-03-18 | Semiconductor Energy Laboratory Co., Ltd. | Parameter extraction method |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
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JP2003108972A (en) * | 2001-07-27 | 2003-04-11 | National Institute Of Advanced Industrial & Technology | Optimum fitting parameter determination method and device therefor, and optimum fitting parameter determination program |
JP2003248810A (en) * | 2002-01-25 | 2003-09-05 | Hewlett Packard Co <Hp> | Method and system for reproduction in genetic optimization system |
JP2005038216A (en) * | 2003-07-16 | 2005-02-10 | Shinka System Sogo Kenkyusho:Kk | Parameter adjusting device |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
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US6269277B1 (en) * | 1998-07-27 | 2001-07-31 | The Leland Stanford Junior University Board Of Trustees | System and method for designing integrated circuits |
JP2000156627A (en) * | 1998-09-18 | 2000-06-06 | Agency Of Ind Science & Technol | Electronic circuit and its adjustment method |
US6314390B1 (en) * | 1998-11-30 | 2001-11-06 | International Business Machines Corporation | Method of determining model parameters for a MOSFET compact model using a stochastic search algorithm |
WO2000065468A2 (en) * | 1999-04-21 | 2000-11-02 | Multisimplex Ab | Process optimation |
-
2005
- 2005-03-22 TW TW094108846A patent/TW200537322A/en unknown
- 2005-03-29 US US10/594,842 patent/US7533356B2/en not_active Expired - Fee Related
- 2005-03-29 EP EP05727788A patent/EP1737041A4/en not_active Withdrawn
- 2005-03-29 KR KR1020067018255A patent/KR20070003911A/en not_active Application Discontinuation
- 2005-03-29 WO PCT/JP2005/005846 patent/WO2005096386A1/en active Application Filing
- 2005-03-29 JP JP2006511664A patent/JPWO2005096386A1/en not_active Withdrawn
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
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JP2003108972A (en) * | 2001-07-27 | 2003-04-11 | National Institute Of Advanced Industrial & Technology | Optimum fitting parameter determination method and device therefor, and optimum fitting parameter determination program |
JP2003248810A (en) * | 2002-01-25 | 2003-09-05 | Hewlett Packard Co <Hp> | Method and system for reproduction in genetic optimization system |
JP2005038216A (en) * | 2003-07-16 | 2005-02-10 | Shinka System Sogo Kenkyusho:Kk | Parameter adjusting device |
Non-Patent Citations (1)
Title |
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See also references of EP1737041A4 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007310873A (en) * | 2006-04-18 | 2007-11-29 | Semiconductor Energy Lab Co Ltd | Parameter extraction method and computer-readable storage medium having program for executing parameter extraction method |
US8676547B2 (en) | 2006-04-18 | 2014-03-18 | Semiconductor Energy Laboratory Co., Ltd. | Parameter extraction method |
Also Published As
Publication number | Publication date |
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US7533356B2 (en) | 2009-05-12 |
TW200537322A (en) | 2005-11-16 |
US20070198103A1 (en) | 2007-08-23 |
EP1737041A1 (en) | 2006-12-27 |
EP1737041A4 (en) | 2009-04-22 |
JPWO2005096386A1 (en) | 2008-02-21 |
KR20070003911A (en) | 2007-01-05 |
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